AI Infrastructure Engineer
New
B
Bright Vision TechnologiesAI Infrastructure
100% Remote (U.S.)Full-TimeSenior
Salary100,000 - 160,000 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- PythonKubernetesPyTorchC++GoLinux
Requirements
- Bachelor’s or Master’s degree in Computer Science or a related field.
- Ten or more years of experience in infrastructure, platform, or HPC engineering.
- Hands-on experience operating GPU clusters or large-scale ML training infrastructure.
- Strong proficiency in Python and at least one systems language such as Go or C++.
- Deep understanding of distributed training, accelerator architectures, and collective communication.
- Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads.
- Strong understanding of Linux internals, networking, and high-performance storage.
- Experience with at least one major cloud provider’s ML infrastructure offerings.
- Strong software engineering practices including testing, CI/CD, and code review.
- Excellent communication and cross-functional collaboration skills.
Responsibilities
- Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations.
- Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams.
- Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering.
- Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate.
- Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication.
- Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics.
- Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale.
- Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing.
- Develop developer tooling and paved-road workflows that let researchers launch experiments safely and efficiently.
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